Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach

Fallahi, Alireza; Pooyan, Mohammad; Lotfi, Nastaran; Baniasad, Fatemeh; Tapak, Leili; Mohammadi-Mobarakeh, Neda; Hashemi-Fesharaki, Seyed Sohrab; Mehvari‐Habibabadi, Jafar; Ay, Mohammad Reza; Nazem-Zadeh, Mohammad-Reza

doi:10.1007/s10072-020-04759-x

Cited by 23 publications

(22 citation statements)

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“…While most studies have used a region-ofinterest (ROI) to extract imaging features, the choice of atlases for ROIs varies. For example, some investigations used traditional automated anatomical labeling (AAL) (Fallahi et al, 2020;Si et al, 2020;Kini et al, 2021), and a different atlas was used in other studies (Gleichgerrcht et al, 2018(Gleichgerrcht et al, , 2020. Zhang et al ( , 2021 used radiomics as a novel method to extract imaging data, and this might provide greater usefulness than conventional methods (Gillies et al, 2016).…”

Section: Methodological Aspects and Future Directionsmentioning

confidence: 99%

“…For example, Park and Ohn (2019) used a random forest classifier for estimating the seizure frequency in TLE through structural MTI features. In addition to classification tasks, the random forest method has been used for the determination of feature importance and selection (Fallahi et al, 2020). Other classification algorithms have also been applied in epilepsy studies, including XGBoost (Torlay et al, 2017), a naïve Baysian classifier (Hwang et al, 2019b), Adaboost (Park et al, 2020), and a quadratic discriminant analysis (Chiang et al, 2015).…”

Section: Classification Modelsmentioning

confidence: 99%

“…As listed in Table 2, there have been various ML approaches to the lateralization of TLE foci, using T1-weighted images, diffusion MRI, FLAIR images, 18F-FDG-PET, or a combination of these (Focke et al, 2012;Keihaninejad et al, 2012;Kerr et al, 2013a,b;An et al, 2014;Hosseini et al, 2014;Chiang et al, 2015;Pustina et al, 2015;Yang et al, 2015;Kamiya et al, 2016;Fang et al, 2017;Mahmoudi et al, 2018;Peter et al, 2018;Bennett et al, 2019;Beheshti et al, 2020a,c;Fallahi et al, 2020;Hosseini et al, 2020). The applications of these approaches to cases without visually detectable lesions, i.e., so-called MRInegative TLE, would be particularly beneficial in clinical settings by providing further clues to the focus beyond the conventional approaches.…”

Section: Lateralization Of Tle Focimentioning

confidence: 99%

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Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Sone

Beheshti

2021

Front. Neurosci.

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Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.

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Section: Methodological Aspects and Future Directionsmentioning

confidence: 99%

Section: Classification Modelsmentioning

confidence: 99%

Section: Lateralization Of Tle Focimentioning

confidence: 99%

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Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

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Beheshti

2021

Front. Neurosci.

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“…Lastly, the adaptive neuro-fuzzy inference system classifies the extracted features. In [133], the laterality in cases of TLE was analyzed by using theoretical graph analysis and ML algorithms. In [134], a comparative study was done on epilepsy detection using various classifiers.…”

Section: ) Ml-based Approaches In Epilepsy Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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“…While machine leaning has provided promising results for the detection of focus in epilepsy, we may need to develop and validate consistent methodology given the diversity of methods ( Sone and Beheshti, 2021 ). Furthermore, network analysis is another trend in epilepsy ( Bernhardt et al, 2015 ), and literature suggested that network metrics derived from neuroimaging could also be used for focus detection when combined with machine learning ( Chiang et al, 2015 ; Yang et al, 2015 ; Kamiya et al, 2016 ; Fallahi et al, 2020 ).…”

Section: Quantitative Analysis and Post-processingmentioning

confidence: 99%

Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy

Sone

2021

Front. Neurosci.

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It has been a clinically important, long-standing challenge to accurately localize epileptogenic focus in drug-resistant focal epilepsy because more intensive intervention to the detected focus, including resection neurosurgery, can provide significant seizure reduction. In addition to neurophysiological examinations, neuroimaging plays a crucial role in the detection of focus by providing morphological and neuroanatomical information. On the other hand, epileptogenic lesions in the brain may sometimes show only subtle or even invisible abnormalities on conventional MRI sequences, and thus, efforts have been made for better visualization and improved detection of the focus lesions. Recent advance in neuroimaging has been attracting attention because of the potentials to better visualize the epileptogenic lesions as well as provide novel information about the pathophysiology of epilepsy. While the progress of newer neuroimaging techniques, including the non-Gaussian diffusion model and arterial spin labeling, could non-invasively detect decreased neurite parameters or hypoperfusion within the focus lesions, advances in analytic technology may also provide usefulness for both focus detection and understanding of epilepsy. There has been an increasing number of clinical and experimental applications of machine learning and network analysis in the field of epilepsy. This review article will shed light on recent advances in neuroimaging for focal epilepsy, including both technical progress of images and newer analytical methodologies and discuss about the potential usefulness in clinical practice.

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Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach

Cited by 23 publications

References 61 publications

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy

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